{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 积累偏差\n",
    "加速度计通常会出现 **偏差** 。这意味着即使加速度为零，它们也会测量到一些非零值。 通过仔细校准（并购买更好的传感器）可以减少偏差，但很难完全消除偏差。\n",
    "\n",
    "在这个 notebook 中，你将简要探讨偏差是如何积累的。\n",
    "\n",
    "----\n",
    "\n",
    "## 第1部分 - 什么是偏差？\n",
    "当传感器的读数持续过高或过低时，它就会产生偏差。还记得电梯加速度数据吗？\n",
    "\n",
    "下面的代码与你用来绘制数据曲线图的代码类似。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "# get elevator data\n",
    "data = np.genfromtxt(\"elevator-lac.csv\", delimiter=\",\")[100:570]\n",
    "\n",
    "# unpack that data\n",
    "t, a_x, a_y, a_z = data.T\n",
    "plt.ylabel(\"Vertical Acceleration (m/s/s)\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "region_delimiters = [5.4, 8.9, 15.6, 18.6]\n",
    "for x_val in region_delimiters:\n",
    "    plt.axvline(x=x_val, color=\"black\",linestyle='dotted')\n",
    "\n",
    "plt.axhline(y=0, color='black',linestyle='dotted')\n",
    "plt.plot(t, a_z) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "需要注意的是，该图的平坦部分（0加速度）没有与点 $a=0$ 线对齐。但它们本应该与之对齐的！\n",
    "\n",
    "这个偏移是由于加速度计中的 **偏差** 造成的。\n",
    "\n",
    "如果我们知道存在偏差，我们可以手动进行纠正。\n",
    "\n",
    "**TODO - 纠正偏差**\n",
    "> 将上面代码单元中的第二行代码更改为以下代码\n",
    "> ```python\n",
    "> plt.plot(t, a_z+0.12) \n",
    "> ```\n",
    "> 然后重新运行该单元格。\n",
    "\n",
    "## 第2部分 - 将偏差对 `离散` 数据的影响可视化 116号 2 304\n",
    "\n",
    "### 用偏差重新编写`get_integral_from_data` 函数\n",
    "\n",
    "在下面的代码单元格中，你将看到我如何重写此函数，使其包含一个 **可选** 的第三个参数 `bias`。 此参数的默认值为零。\n",
    "\n",
    "当这个参数非零时，它会模拟（或纠正）数据中的偏移量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_integral_from_data(acceleration_data, times, bias=0.0):\n",
    "    \"\"\"\n",
    "    Numerically integrates data AND artificially introduces \n",
    "    bias to that data.\n",
    "    \n",
    "    Note that the bias parameter can also be used to offset\n",
    "    a biased sensor.\n",
    "    \"\"\"\n",
    "    accumulated_speed = 0.0\n",
    "    last_time = times[0]\n",
    "    speeds = []\n",
    "    for i in range(1, len(times)):\n",
    "        \n",
    "        # THIS is where the bias is introduced. No matter what the \n",
    "        # real acceleration is, this biased accelerometer adds \n",
    "        # some bias to the reported value.\n",
    "        acceleration = acceleration_data[i] + bias\n",
    "        \n",
    "        time = times[i]\n",
    "        delta_t = time - last_time\n",
    "        delta_v = acceleration * delta_t\n",
    "        accumulated_speed += delta_v\n",
    "        speeds.append(accumulated_speed)\n",
    "        last_time = time\n",
    "    return speeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TODO - 绘制在有无偏差情况下的速度和加速度曲线图**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO \n",
    "#   0. read through the code below to get a sense for what it does.\n",
    "#   1. run this cell with OFFSET = 0.0\n",
    "#   2. look at the graph. Note how drastic the error is with the\n",
    "#      integrated speed! \n",
    "#   3. change OFFSET to OFFSET = 0.12 and re-run the cell\n",
    "\n",
    "OFFSET = 0.0\n",
    "\n",
    "plt.title(\"The Effect of Bias on Integrated Data\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.ylabel(\"Acceleration (m/s/s) AND Speed (m/s)\")\n",
    "\n",
    "# get the elevator speeds by integrating the acceleration data\n",
    "elevator_speeds = get_integral_from_data(a_z, t, OFFSET)\n",
    "\n",
    "# plot acceleration data\n",
    "plt.plot(t, a_z+OFFSET) \n",
    "\n",
    "# plot INFERRED (from integration) speed data\n",
    "plt.plot(t[1:], elevator_speeds)\n",
    "\n",
    "# add a legend to the plot\n",
    "plt.legend([\"Acceleration\", \"Speed\"])\n",
    "\n",
    "# vertical lines\n",
    "region_delimiters = [5.4, 8.9, 15.6, 18.6]\n",
    "for x_val in region_delimiters:\n",
    "    plt.axvline(x=x_val, color=\"black\",linestyle='dotted')\n",
    "\n",
    "# a=0 reference line\n",
    "plt.axhline(y=0, color='black',linestyle='dotted')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当`OFFSET = 0.0`时，速度曲线图看起来很荒唐！ 它显示，在旅程结束时，电梯的速度是-1.8米/秒（显然它应该是0.0）。\n",
    "\n",
    "请注意，随着时间的推移，偏差的影响会变得越来越大（对于速度曲线图来说）！\n",
    "\n",
    "当 `OFFSET = 0.12`时，这个图虽然仍然不完美，但看起来更合理。\n",
    "\n",
    "> 当集成发生很长一段时间时，我们应对集成数据持更加怀疑的态度。\n",
    "\n",
    "## 第3部分 - 重复集成的影响\n",
    "\n",
    "当我们给数据时间积累时，你会发现这种偏差会对集成数据产生更大的影响。 如果我们集成 **两次**，会发生什么情况呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is just a helper function for integrating twice.\n",
    "def double_integral(data, times, bias=0.0):\n",
    "    print(bias)\n",
    "    speeds = get_integral_from_data(data, times, bias)\n",
    "    displacements = get_integral_from_data(speeds, times[1:])\n",
    "    return displacements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TODO  - 观察偏差对双重积分的影响**\n",
    "\n",
    "> 1. 运行下面的单元格（OFFSET = 0.12）。它表明电梯上升到约8米的高度。\n",
    "> 2. 将OFFSET更改为0.0并重新运行。看看这个推断的位置有多糟糕！ 看起来我们实际上已经在下降了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "OFFSET = 0.12\n",
    "\n",
    "plt.title(\"Elevator Height vs Time\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.ylabel(\"Elevator Height (meters)\")\n",
    "\n",
    "displacements = double_integral(a_z, t, OFFSET)\n",
    "plt.plot(t[2:], displacements)\n",
    "\n",
    "# vertical lines\n",
    "region_delimiters = [5.4, 8.9, 15.6, 18.6]\n",
    "for x_val in region_delimiters:\n",
    "    plt.axvline(x=x_val, color=\"black\",linestyle='dotted')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第4部分 - 并行停车\n",
    "\n",
    "相同的练习，不同的数据。请注意，这次的加速计是“完美的”（因为这里的数据是假的），我们将使用`bias`参数来*引入* 偏差（而不是纠正它）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers import process_data\n",
    "\n",
    "PARALLEL_PARK_DATA = process_data(\"parallel_park.pickle\")\n",
    "\n",
    "TIMESTAMPS    = [row[0] for row in PARALLEL_PARK_DATA]\n",
    "DISPLACEMENTS = [row[1] for row in PARALLEL_PARK_DATA]\n",
    "YAW_RATES     = [row[2] for row in PARALLEL_PARK_DATA]\n",
    "ACCELERATIONS = [row[3] for row in PARALLEL_PARK_DATA]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 观察偏差的影响\n",
    "运行下面的代码单元格。你会发现，当偏差很小（如0.01）时，加速度计读出的加速度（蓝点）与实际加速度（橙色点）非常吻合。\n",
    "\n",
    "当偏差增加时，情况会很快变得很糟糕！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try running this cell with BIAS set to 0.01, 0.05, 0.1, 0.2, 0.4\n",
    "# and so on. Even though these are all small numbers the effect of \n",
    "# bias tends to accumulate as the sensor runs for longer and longer.\n",
    "\n",
    "BIAS = 0.01\n",
    "\n",
    "integrated_displacements = double_integral(ACCELERATIONS, TIMESTAMPS, BIAS)\n",
    "\n",
    "plt.title(\"Calculated position (blue) vs Actual position (orange)\")\n",
    "plt.xlabel(\"Time (seconds)\")\n",
    "plt.ylabel(\"Position (meters)\")\n",
    "plt.scatter(TIMESTAMPS[2:], integrated_displacements, alpha=0.5)\n",
    "plt.scatter(TIMESTAMPS, DISPLACEMENTS, alpha=0.5)\n",
    "plt.legend([\"Calculated (from integral)\", \"Actual position\"])\n",
    "plt.show()"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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  "toc": {
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